When comparing two objects, conventional approaches focus on a lengthy analysis of associated feature vectors. For example, if a first object is represented by feature vector, U, and a second object is represented by feature vector, V, then the conventional approach is to compare the dimensions of U to corresponding dimensions of V in order to determine distance measures.
For example, suppose the two objects in question are both 16×16 pixel images. In that case, both U and V will have 256 dimensions, one for each pixel, and each of those dimensions will include a representative pixel value that reflect the encoded characteristics of the pixel (e.g., an RGB value). As such, the distance between the first pixel of U (e.g., u1) and the first pixel of V (e.g., v1) can be computed based upon a difference between the values of u1 and v1. If the values for u1 and v1 are similar, then the calculated distance will be small. Similar comparisons can be made for all 256 pixels and a total distance between U and V can be determined based upon a sum of the individual distances for each of the dimensions of U and V. This total distance between U and V can be representative of the consistency between the two objects.
Conventional distance measures focus on the exact values of the dimensions and are therefore sensitive to changes in or differences between the values. However, many types of comparisons are less sensitive to dimension values and more sensitive to other characteristics. Furthermore, computations based upon the values can be resource-intensive; particularly when the number of dimensions for U and V are large or when corresponding values of the dimensions are large or complex.